The advent of Transformer-based Large Language Models (LLMs) has led to an unprecedented surge of AI-generated text (AIGT) across online platforms and academic domains. While these models exhibit near-human fluency and stylistic coherence, their widespread adoption has raised concerns about authorship integrity, research quality, and the recursive contamination of training corpora with synthetic data. These developments underscore the need for reliable AIGT detection methods and benchmark datasets, particularly for malicious or deceptive *ghostwriting* scenarios where AIGT is intentionally crafted to evade detection. To address this, we present **GhostWriter**, a large-scale, bilingual (German and English), multi-generator, and multi-domain dataset for AIGT detection. The dataset comprises human- and AI-authored texts produced under domain-specific *ghostwriting* conditions, including examples intentionally embedded within otherwise human-written texts to obscure their AI origin. With **GhostWriter**, we (i) aim to expand the resources available for German AIGT datasets, (ii) emphasize mixed or fused synthesizations—since most existing corpora are limited to the document level—and (iii) introduce specifically crafted malicious ghostwriting scenarios across multiple domains and generators.

Analysing texts spanning long periods of time is critical for researchers in historical linguistics and related disciplines. However, publicly available corpora suitable for such analyses are scarce. The Project Gutenberg (PG) corpus presents a significant yet underutilized opportunity in this context, due to the absence of accurate temporal metadata. We take advantage of language models and information retrieval to explore four sources of information – Open Web, Wikipedia, Open Library API, and PG books texts – to add missing temporal metadata to the PG corpus. Through 20 experiments employing state-of-the-art Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) methods, we estimate the production years of all PG books. We curate an enriched metadata repository for the PG corpus and propose a refined version for it, which includes 53,774 books with a total of 3.8 billion tokens in 11 languages, produced between 1600 and 2000. This work provides a new resource for computational linguistics and humanities studies focusing on diachronic analyses. The final dataset and all experiments data are publicly available (https://github.com/OmarMomen14/pg-dates).